Logistics distribution path optimization method and device, computer equipment and medium

Abstract

The invention provides a logistics distribution path optimization method and device, computer equipment and a medium, and belongs to the field of logistics distribution, and the method comprises the steps: obtaining the position coordinates of all distribution centers and stations, and determining the center-of-gravity position and the midpoint position of the connection line of the adjacent distribution centers according to the position coordinates of the distribution centers; constructing a ray equation through the center-of-gravity position and each midpoint position, and dividing a plurality of fan-shaped partitions covering all stations; substituting the position coordinates of the stations into a ray equation, and determining a unique fan-shaped partition to which each station belongs; constructing an ant colony algorithm based on each fan-shaped partition, setting at least one ant at each site and each distribution center, setting ant path selection probability formula parameters according to the current time period, iterating the ant colony algorithm, and under the condition that the maximum number of iterations is reached, determining that the ant colony algorithm starts from the distribution center according to the pheromone concentration; and sequentially connecting the stations and returning to the distribution path of the distribution center. And the problem of low efficiency of traditional logistics is comprehensively solved.

Description

Technical Field

[0001] The present invention belongs to the field of logistics distribution, and specifically relates to a logistics distribution path optimization method, device, computer equipment and medium. Background Art

[0002] In today's increasingly globalized world, the business environment is complex and ever-changing, and companies are facing numerous challenges. On the one hand, as the market continues to expand, customer demand is also growing, and the requirements for efficient and timely logistics and distribution are becoming increasingly stringent.

[0003] However, the drawbacks of traditional logistics and distribution methods are becoming increasingly apparent in the face of complex environmental changes. For example, the traditional method calculates the logistics cost of transporting goods from each supply warehouse to the logistics address's final destination based on the location information of the supply warehouse and the logistics address's final destination. Based on the logistics cost and transportation losses of each supply warehouse to the logistics address's final destination, a comprehensive analysis is conducted to select the optimal supply warehouse for the transportation route point. Initial transportation routes are then planned, and the supply routes are then adjusted based on the load supply capacity of each supply warehouse.

[0004] The traditional method of full-coverage route screening has high screening costs. In addition, due to the lack of analysis and optimized scheduling of distribution data, distribution vehicles often face problems such as detours and traffic jams, resulting in increasingly low distribution efficiency. In particular, the costs of transportation, warehousing and other links remain high, seriously suppressing the profit margins of enterprises. Summary of the Invention

[0005] In order to solve the above problems, the present invention provides a logistics distribution path optimization method, device, computer equipment and medium.

[0006] In order to achieve the above object, the present invention provides the following technical solutions: A method for optimizing a logistics distribution path, comprising: Obtain the location coordinates of all distribution centers and sites, and determine the center of gravity and the midpoint of the line connecting adjacent distribution centers based on the location coordinates of the distribution centers; A ray equation is constructed by using the center of gravity position and the midpoint positions to divide the area into multiple sector partitions covering all sites; the location coordinates of the sites are substituted into the ray equation to determine the unique sector partition to which each site belongs; An ant colony algorithm is constructed based on each sector partition. At least one ant is set at each site and distribution center. The parameters of the ant path selection probability formula are set according to the current time period. The ant colony algorithm is iterated. When the maximum number of iterations is reached, the distribution path starting from the distribution center, connecting each site in sequence and returning to the distribution center is determined according to the pheromone concentration.

[0007] Optionally, the formula for determining the center of gravity position based on the location coordinates of the distribution center is: ; in, is the coordinate of the distribution center, n is the number of sites, O The center of gravity position.

[0008] Optionally, setting the ant path selection probability formula parameters according to the current time period includes: Set the importance parameters of the heuristic information according to the current time period; Construct the ant path selection probability formula, specifically: ; in, is the pheromone concentration on the path from i to j, t is the time step, is the inverse of the distance of the moving path, is the set of sites that ant k is going to, is the pheromone concentration of the candidate path, is the inverse of the distance of the candidate path, α is the importance parameter of pheromone, β is the importance parameter of the heuristic information; i is the current site or distribution center, j is the site or distribution center to be visited, and s is the set of sites to be visited.

[0009] Optionally, iterating the ant colony algorithm includes: The pheromone concentration is updated at each iteration, and the update formula is: ; ; in, is the volatility coefficient, is the pheromone concentration on the path from i to j in this cycle; n is the number of iterations, t is the time step, is the pheromone concentration accumulated in this cycle, is the pheromone concentration left by the kth ant on the moving path in this cycle, and m is the total number of ants.

[0010] Optionally, obtaining the location coordinates of all distribution centers and sites includes: The judgment matrix is ​​constructed based on the actual conditions of multiple candidate sites through the analytic hierarchy process; Determine the weight of each candidate site based on the judgment matrix; Determine the candidate site with the largest weight as the site and obtain its coordinate location.

[0011] A logistics distribution path optimization device, comprising: The acquisition module is used to obtain the location coordinates of all distribution centers and sites, and determine the center of gravity and the midpoint of the line connecting adjacent distribution centers based on the location coordinates of the distribution centers; A partitioning module is configured to construct a ray equation using the center of gravity position and each midpoint position to partition the site into a plurality of sector partitions covering all sites; and substitute the site's position coordinates into the ray equation to determine the unique sector partition to which each site belongs. The update module is used to construct an ant colony algorithm based on each sector partition, set at least one ant at each site and distribution center, set the parameters of the ant path selection probability formula according to the current time period, iterate the ant colony algorithm, and when the maximum number of iterations is reached, determine the distribution path starting from the distribution center, connecting each site in sequence and returning to the distribution center based on the pheromone concentration.

[0012] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned logistics distribution path optimization method.

[0013] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned logistics distribution path optimization method is implemented.

[0014] The logistics distribution path optimization method provided by the present invention has the following beneficial effects: This invention uses a centroid zoning method to precisely divide independent delivery areas, completely eliminating redundant routes and vehicle scheduling conflicts in cross-regional delivery, ensuring that the scope of responsibility for each site is fully defined. Secondly, it improves the dynamic response mechanism of the ant colony algorithm, incorporating the influence of traffic hours to effectively avoid congested roads and significantly shorten delivery time. Simultaneously, through the intra-zone path optimization model, it continuously accumulates optimal path experience, significantly reducing overall delivery mileage. The synergistic effect of these technologies significantly increases vehicle load factors and effectively reduces empty driving rates and operating costs. Ultimately, this creates an efficient, stable, and environmentally friendly logistics and distribution system that not only reduces the cost of route optimization but also achieves high transportation efficiency, ensures timely delivery, achieves green transportation goals, and comprehensively addresses the inefficiency of traditional logistics. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] To more clearly illustrate the embodiments of the present invention and its design, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort.

[0016] Figure 1The figure is a flow chart of a method for optimizing a logistics distribution path according to an exemplary embodiment of the present invention.

[0017] Figure 2 The present invention provides an algorithm flow chart of an ant colony algorithm according to an exemplary embodiment.

[0018] Figure 3 This is a block diagram of a logistics distribution path optimization device provided according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the technical solution of the present invention and to be able to implement it, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention and are not intended to limit the scope of protection of the present invention.

[0020] The technical solutions provided by various embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0021] First, the present invention provides a method for optimizing logistics distribution paths. Figure 1 As shown, the following steps are included: S101. Obtain the location coordinates of all distribution centers and sites, and determine the center of gravity and the midpoint of the line connecting adjacent distribution centers based on the location coordinates of the distribution centers.

[0022] In this step, distribution centers are fixed locations, and the coordinates of each distribution center are directly obtained. The selection of distribution sites needs to be based on the city's environment and actual conditions. In today's highly competitive logistics industry, the rational selection of distribution sites is crucial for improving operational efficiency, reducing costs, and enhancing service quality. The Analytic Hierarchy Process (AHP), as a multi-criteria decision-making method, effectively considers various factors and provides a scientific and systematic basis for the selection of distribution sites.

[0023] In one embodiment, a judgment matrix can be constructed based on the actual conditions of multiple candidate sites using a hierarchical analysis method; the weight of each candidate site is determined according to the judgment matrix; the candidate site with the largest weight is determined as the site and its coordinate position is obtained.

[0024] For example, the analytic hierarchy process (AHP) first decomposes complex decision-making problems into the goal layer, the criteria layer, and the solution layer. In the selection of logistics distribution sites, the goal layer is to select the optimal logistics distribution site. The criteria layer typically covers several important factors, such as transportation accessibility, which directly affects the speed and cost of goods transportation. Sites near highways, railway hubs, or ports can significantly reduce transportation time and costs. Cost factors, including land acquisition costs, construction costs, and long-term operating costs, have a significant impact on the company's economic performance. Service coverage: Sites should be able to effectively cover the target customer base to ensure timely and comprehensive delivery. Infrastructure conditions, such as warehouse area, reasonable layout, and advanced loading and unloading equipment, directly determine the site's operational efficiency. The solution layer represents the specific logistics distribution sites to be selected.

[0025] Next, we construct a judgment matrix. Experts or experienced practitioners in related fields are invited to compare each criterion's relative importance to the objective, assigning values ​​using a 1-9 scale to form a criterion-level judgment matrix. For example, if accessibility is considered slightly more important than cost, the corresponding value is 3; otherwise, it is 1 / 3. By calculating the eigenvectors and maximum eigenvalues ​​of the judgment matrix and performing consistency checks to ensure the rationality and reliability of the judgments, the weights of each criterion are determined.

[0026] At the solution level, a judgment matrix is ​​constructed for each criterion. For example, for the accessibility criterion, factors such as distance from major transportation routes and traffic congestion are compared for each candidate site. The relative importance of these factors is determined and assigned to construct a matrix, calculating the weight of each site in terms of accessibility. Similarly, the solution-level judgment matrix is ​​calculated and consistency-checked for criteria such as cost, service coverage, and infrastructure conditions, resulting in the weight of each site under the corresponding criterion.

[0027] Finally, the weights of each criterion at the solution level are combined with the weights at the criterion level to determine the overall weight of each candidate site. The site with the highest overall weight is the optimal logistics distribution site. For example, consider three candidate sites, A, B, and C. Site A performs well in terms of accessibility and infrastructure, Site B offers advantages in terms of cost, and Site C offers a wider service coverage area. After a comprehensive evaluation of the weights of each criterion, if Site A has the highest overall weight, then it is the logistics distribution site that best meets the company's needs.

[0028] In summary, the analytic hierarchy process fully considers the multiple factors and their interrelationships in the selection of logistics distribution sites through systematic analysis and comprehensive evaluation, avoids the one-sidedness of single-factor decision-making, and can help logistics companies make more scientific and reasonable site selection decisions in complex environments, thereby optimizing the logistics distribution network and improving the overall competitiveness and operational efficiency of the company.

[0029] After determining the site, it is necessary to determine the center of gravity location and the midpoint of the line connecting adjacent distribution centers based on all distribution centers. Due to the large scale of cities, logistics distribution vehicles are plagued by congestion, making timeliness difficult to ensure and increasing costs. Inspired by the concept of newspaper distribution services, the concept of zoning has been introduced into vehicle scheduling. Common zoning methods include scanning zoning. This invention introduces a center of gravity zoning method suitable for non-linear site distribution. Each zone has its own dedicated site and no overlapping services.

[0030] The center of gravity zoning method first requires clarifying the center of gravity and the midpoint of the line connecting any two adjacent distribution centers.

[0031] Specifically, identify the center of gravity. Assume there are n distribution centers , its center of gravity is:

[0032] ; in, is the coordinate of the site, d is the number of distribution centers, O The center of gravity position.

[0033] Determine the midpoint of the line connecting two adjacent distribution centers , and each distribution center has at least one connection center with the other two adjacent distribution centers.

[0034] S102, constructing a ray equation using the center of gravity position and each midpoint position to divide the site into multiple sector partitions covering all sites; substituting the site's position coordinates into the ray equation to determine the unique sector partition to which each site belongs.

[0035] Through the center of gravity O and each midpoint Draw the ray and determine the ray equation ≤ ≤z). The fan-shaped area formed by two adjacent rays is used as a partition, and the stations on the ray belong to the partition on the left side of the ray. Substitute the coordinates of each station into the ray equation , and determine the partition to which it belongs through linear programming method.

[0036] The centroid partitioning method clearly determines the number of partitions, and each partition has a unique station, each corresponding to a completely determined station. The services of each station are independent and non-overlapping, significantly reducing computational complexity. This is particularly advantageous for large-scale vehicle scheduling problems.

[0037] S103. Construct an ant colony algorithm based on each sector partition, set at least one ant at each site and distribution center, set the parameters of the ant path selection probability formula according to the current time period, iterate the ant colony algorithm, and when the maximum number of iterations is reached, determine the distribution path starting from the distribution center, connecting each site in sequence and returning to the distribution center based on the pheromone concentration.

[0038] The logistics distribution path optimization problem belongs to NP problem. Considering the characteristics of the model and the actual application, the present invention decided to adopt a positive feedback algorithm with strong robustness and very fast solution speed, namely the ant colony algorithm. The present invention adjusts the α, β and This adjustment is to avoid the influence of the pheromone importance factor α on the accurate reception of pheromones by the ant colony and the influence of the heuristic function importance factor β. This has a significant impact on ant colonies' route selection. Slow evaporation allows the ants to better absorb the pheromone, while rapid evaporation can cause them to be at a loss when choosing their next route. To more accurately adapt to urban roads, the present invention also sets the importance parameter of the heuristic information based on the current time period. Specifically, the β value is dynamically adjusted according to the traffic time, and the β value is dynamically increased to the upper limit during peak traffic hours to enhance the path distance weight.

[0039] Optimize the logistics distribution path based on the ant colony algorithm, such as Figure 2 As shown, the specific algorithm steps are as follows: Based on each sector partition, the ant colony algorithm is constructed separately. First, the parameter values ​​and position coordinates are set, and the values ​​are adjusted reasonably. Set the starting time t=0, and the pheromone at the initial moment is , where i is the current site or distribution center, and j is the destination site or distribution center.

[0040] Initialize the ant colony: randomly place m ants at the distribution center and n-1 sites, ensuring that there is an ant at each site, that is, m is greater than or equal to n.

[0041] Ant Path Selection: Each ant chooses a path based on certain rules. The probability of an ant choosing a path is generally related to the pheromone concentration and distance along the path. Because paths with high pheromone concentrations and those previously traveled by previous ants are more likely to be chosen, a probability conversion rule is introduced.

[0042] The rules are as follows: ; in, is the pheromone concentration on the path from i to j, t is the time step, is the inverse of the distance of the moving path, is the set of sites that ant k is going to, is the pheromone concentration of the candidate path, is the inverse of the distance of the candidate path, α is the importance parameter of pheromone, β is the importance parameter of heuristic information; i is the current site or distribution center, j is the site or distribution center to be visited, and s is the set of sites to be visited.

[0043] Update pheromone concentration: After all ants have completed their movement, the pheromone concentration is updated according to their paths, mainly updating the pheromone concentration on the paths passed by the ants.

[0044] The pheromone concentration is updated at each iteration and the pheromone concentration update follows the following rules: ; ; in, is the volatility coefficient, 0< <1, the present invention takes 0.4; 1-rho is the pheromone residue ordinal number; is the pheromone concentration on the path from i to j in this cycle; n is the number of iterations, t is the time step, is the pheromone concentration accumulated in this cycle. The basic model is: ; Where: Q is a constant, representing the pheromone intensity; is the path length at time k.

[0045] Determine whether the maximum number of cycles (maximum number of iterations) has been reached. If so, the optimal route is the one with the highest pheromone concentration, starting from the distribution center, connecting each station in sequence, and returning to the distribution center. Otherwise, continue iterating. Finally, output the results, and based on the final pheromone distribution, output the optimal solution or approximate solution found.

[0046] This approach, based on a centroid-based zoning method, precisely demarcates independent delivery areas, completely eliminating redundant routes and vehicle scheduling conflicts in cross-regional deliveries and ensuring clear responsibilities for each station. Furthermore, the dynamic response mechanism of the ant colony algorithm is improved to account for traffic hours, effectively avoiding congested roads and significantly reducing delivery time. Furthermore, a route optimization model within each zone continuously accumulates optimal route experience, significantly reducing overall delivery mileage. The synergistic effect of these technologies significantly increases vehicle load rates, effectively reducing empty driving rates and operating costs. The result is an efficient, stable, and environmentally friendly logistics and distribution system that not only ensures timely delivery but also achieves green transportation goals through the dispatch of new energy vehicles, comprehensively addressing the inefficiencies of traditional logistics.

[0047] To verify the effectiveness of the ant colony algorithm-based logistics routing optimization model, we conducted a systematic simulation experiment. We selected a real-world distribution scenario and constructed a 21-node logistics distribution network model. The experimental data was based on the distribution center coordinates (17, 34). The 21 distribution nodes were numbered 1-21 based on actual Geographic Information System (GIS) data. The node coordinates are detailed in Table 1.

[0048] Set the initial pheromone importance factor α and heuristic function importance factor β, pheromone volatility factor Rho, Q represents pheromone intensity, number of sites Nun_customer, and maximum number of iterations Max_iterations, as shown in Table 2.

[0049] Table 1 Basic information of the site Table 2 Related parameter settings In the logistics system optimization study, we first established an unpartitioned distribution model. After calculation and analysis, the distribution center was located at coordinate point O(17,34). The distribution route and total mileage data for the unpartitioned model are as follows: the total mileage reached 156 kilometers.

[0050] After further implementing the regional division strategy, a three-region collaborative distribution plan was constructed: The total mileage of the optimized path in area A is 44.17 kilometers.

[0051] The optimized route in area B has a total mileage of 54.15 kilometers.

[0052] The optimized route in area C has a total mileage of 40.44 kilometers.

[0053] Experimental data validated the effectiveness of this optimization scheme: total delivery mileage was significantly reduced after zoning, achieving an 11.2% reduction compared to the original scheme. Based on a time-distance linear correlation model, average delivery times were also reduced, demonstrating the significant value of zoning strategies in improving logistics efficiency.

[0054] As the number of iterations increases, the target value shows a gradual downward trend. This indicates that the ant colony algorithm is continuously optimizing the solution and approaching a more optimal target value. In the early stages of the iterations, the target value decreases rapidly. This is because the ant colony algorithm can quickly explore the solution space and find some good solutions in the initial stages. As the number of iterations increases, the rate of decrease in the target value gradually slows down. This may be due to the algorithm gradually converging, and the room for solution improvement becomes smaller. At certain iterations, the target value may increase slightly. This may be due to the introduction of some random factors in the algorithm's search process, resulting in a temporary decrease in the quality of the solution. However, overall, the target value continues to improve.

[0055] In summary, after in-depth analysis and practical verification, we can clearly conclude that the zoned logistics distribution model offers significant advantages over the unzoned distribution model. In practice, zoned logistics distribution demonstrates superior performance in multiple aspects. First, zoned logistics distribution significantly optimizes delivery routes. By rationally dividing the zones, delivery routes can be planned more scientifically, reducing unnecessary detours and duplicate trips, significantly improving delivery efficiency and shortening delivery time. Second, zoned logistics distribution effectively reduces delivery costs. This optimized route reduces transportation mileage and time, thereby reducing vehicle wear and tear, labor costs, and other expenses, thereby contributing to energy conservation and environmental protection. This optimized distribution model not only improves economic benefits for enterprises but also provides customers with a more efficient and convenient service experience.

[0056] In this way, in this plan, the distribution routes of each region are rationally planned by combining the center of gravity zoning method, and the hierarchical analysis method is used to select the location of regional distribution centers, realizing the efficient operation of three-region collaborative distribution. Finally, based on the ant colony algorithm, the foraging behavior of ants is simulated, and the relatively optimal solution is found in the complex path combination through the accumulation and updating of pheromones.

[0057] Secondly, the present invention also provides a logistics distribution path optimization device, such as Figure 3 Shown, including: The acquisition module 201 is used to acquire the location coordinates of all distribution centers and sites, and determine the center of gravity and the midpoint of the line connecting adjacent distribution centers based on the location coordinates of the distribution centers.

[0058] The partitioning module 202 is used to construct a ray equation through the center of gravity position and each midpoint position to divide the site into multiple sector partitions covering all sites; substitute the site position coordinates into the ray equation to determine the unique sector partition to which each site belongs.

[0059] The update module 203 is used to construct an ant colony algorithm based on each sector partition, set at least one ant at each site and distribution center, set the parameters of the ant path selection probability formula according to the current time period, iterate the ant colony algorithm, and when the maximum number of iterations is reached, determine the distribution path starting from the distribution center, connecting each site in sequence and returning to the distribution center based on the pheromone concentration.

[0060] Using the aforementioned device, the present invention precisely divides independent delivery areas based on the centroid zoning method, completely eliminating redundant routes and vehicle scheduling conflicts in cross-regional delivery, ensuring that the scope of responsibility for each site is completely clear. Secondly, the dynamic response mechanism of the ant colony algorithm is improved to incorporate the influence of traffic time periods, effectively avoiding congested sections of road and significantly shortening delivery time. Simultaneously, through the intra-zone path optimization model, the optimal path experience is continuously accumulated, significantly reducing the overall delivery mileage. The synergistic effect of these technologies significantly increases vehicle load factors and effectively reduces empty driving rates and operating costs. Ultimately, an efficient, stable, and environmentally friendly logistics and distribution system is established, which not only reduces the cost of path optimization but also achieves high transportation efficiency, ensures timely delivery, achieves green transportation goals, and comprehensively addresses the inefficiency of traditional logistics.

[0061] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 The steps of the logistics distribution path optimization method are provided.

[0062] The present invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 The steps of the logistics distribution path optimization method are provided.

[0063] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0065] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0067] It should be noted that the specific embodiments described above can enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although this specification has described the present invention in detail, those skilled in the art should understand that the present invention can still be modified or replaced with equivalents; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are included in the scope of protection of the patent for the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A logistics distribution path optimization method, characterized in that: The method comprises: Obtain the location coordinates of all distribution centers and sites, and determine the center of gravity and the midpoint of the line connecting adjacent distribution centers based on the location coordinates of the distribution centers; A ray equation is constructed by using the center of gravity position and the midpoint positions to divide the area into multiple sector partitions covering all sites; the location coordinates of the sites are substituted into the ray equation to determine the unique sector partition to which each site belongs; An ant colony algorithm is constructed based on each sector partition. At least one ant is set at each site and distribution center. The parameters of the ant path selection probability formula are set according to the current time period. The ant colony algorithm is iterated. When the maximum number of iterations is reached, the distribution path starting from the distribution center, connecting each site in sequence and returning to the distribution center is determined according to the pheromone concentration.

2. The method according to claim 1, characterized in that The formula for determining the center of gravity based on the location coordinates of the distribution center is: ; in, is the coordinate of the distribution center, d is the number of distribution centers, O The center of gravity position.

3. The method according to claim 2, characterized in that The method of setting the ant path selection probability formula parameters according to the current time period includes: Set the importance parameters of the heuristic information according to the current time period; Construct the ant path selection probability formula, specifically: ； in, is the pheromone concentration on the path from i to j, t is the time step, is the inverse of the distance of the moving path, is the set of sites that ant k is going to, is the pheromone concentration of the candidate path, is the inverse of the distance of the candidate path, α is the importance parameter of pheromone, β is the importance parameter of the heuristic information; i is the current site or distribution center, j is the site or distribution center to be visited, and s is the set of sites to be visited.

4. The method according to claim 3, characterized in that Iterating the ant colony algorithm includes: The pheromone concentration is updated at each iteration, and the update formula is: ； ； in, is the volatility coefficient, is the pheromone concentration on the path from i to j in this cycle; n is the number of iterations, t is the time step, is the pheromone concentration accumulated in this cycle, is the pheromone concentration left by the kth ant on the moving path in this cycle, and m is the total number of ants.

5. The method according to claim 1, wherein Obtaining the location coordinates of all distribution centers and sites includes: The judgment matrix is ​​constructed based on the actual conditions of multiple candidate sites through the analytic hierarchy process; Determine the weight of each candidate site based on the judgment matrix; Determine the candidate site with the largest weight as the site and obtain its coordinate location.

6. A logistics distribution path optimization device, characterized in that: The device comprises: The acquisition module is used to obtain the location coordinates of all distribution centers and sites, and determine the center of gravity and the midpoint of the line connecting adjacent distribution centers based on the location coordinates of the distribution centers; A partitioning module is configured to construct a ray equation using the center of gravity position and each midpoint position to partition the site into a plurality of sector partitions covering all sites; and substitute the site's position coordinates into the ray equation to determine the unique sector partition to which each site belongs. The update module is used to construct an ant colony algorithm based on each sector partition, set at least one ant at each site and distribution center, set the parameters of the ant path selection probability formula according to the current time period, iterate the ant colony algorithm, and when the maximum number of iterations is reached, determine the distribution path starting from the distribution center, connecting each site in sequence and returning to the distribution center based on the pheromone concentration.

7. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

8. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 5 when executing the program.

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